In [ ]:
df_data.groupby('label')['label'].count().plot.bar(
title='class distribution'
)
Out[ ]:
<Axes: title={'center': 'class distribution'}, xlabel='label'>
import numpy as np
import pandas as pd
import os
import random
import shutil
import glob
import yaml
import torch
import matplotlib.pyplot as plt
import xml.etree.ElementTree as ET
import cv2
from PIL import Image
from matplotlib import pyplot as plt
from matplotlib import patches
from pathlib import Path
from sklearn.model_selection import train_test_split
import ultralytics
from ultralytics import YOLO
ultralytics.checks()
Ultralytics YOLOv8.0.218 🚀 Python-3.10.13 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB) Setup complete ✅ (20 CPUs, 125.7 GB RAM, 95.5/455.5 GB disk)
directory = "/workspace/kaggle"
image_directory = directory + "/images"
annotation_directory = directory + "/annotations"
annotations = list(Path(annotation_directory).glob(r'**/*{}'.format('xml')))
class_id = {
"with_mask" : 0,
"mask_weared_incorrect" : 1,
"without_mask" : 2
}
data_dict = {
'filename': [],
'label': [],
'class_id': [],
'width': [],
'height': [],
'bboxes': []
}
for annotation_path in annotations:
tree = ET.parse(annotation_path)
root = tree.getroot()
filename = root.find('filename').text
for obj in root.findall("object"):
label = obj.find("name").text
bbox = []
# bndbox has xmin, ymin, xmax, ymax
bndbox_tree = obj.find('bndbox')
bbox.append(int(bndbox_tree.find('xmin').text))
bbox.append(int(bndbox_tree.find('ymin').text))
bbox.append(int(bndbox_tree.find('xmax').text))
bbox.append(int(bndbox_tree.find('ymax').text))
size = root.find('size')
data_dict['filename'].append(filename)
data_dict['width'].append(int(size.find('width').text))
data_dict['height'].append(int(size.find('height').text))
data_dict['label'].append(label)
data_dict['class_id'].append(class_id[label])
data_dict['bboxes'].append(bbox)
df_data = pd.DataFrame(data_dict)
df_data.head()
| filename | label | class_id | width | height | bboxes | |
|---|---|---|---|---|---|---|
| 0 | maksssksksss659.png | mask_weared_incorrect | 1 | 441 | 575 | [113, 97, 252, 251] |
| 1 | maksssksksss799.png | with_mask | 0 | 400 | 300 | [29, 66, 47, 82] |
| 2 | maksssksksss799.png | with_mask | 0 | 400 | 300 | [45, 41, 65, 63] |
| 3 | maksssksksss799.png | with_mask | 0 | 400 | 300 | [11, 217, 65, 263] |
| 4 | maksssksksss799.png | with_mask | 0 | 400 | 300 | [84, 239, 134, 285] |
df_data.isna().sum()
filename 0 label 0 class_id 0 width 0 height 0 bboxes 0 dtype: int64
df_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4072 entries, 0 to 4071 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 filename 4072 non-null object 1 label 4072 non-null object 2 class_id 4072 non-null int64 3 width 4072 non-null int64 4 height 4072 non-null int64 5 bboxes 4072 non-null object dtypes: int64(3), object(3) memory usage: 191.0+ KB
df_data.label.unique()
array(['mask_weared_incorrect', 'with_mask', 'without_mask'], dtype=object)
print(f"Total 'without_mask' labels: {sum(df_data.label == 'without_mask')}")
print(f"Total 'mask_weared_incorrect' labels: {sum(df_data.label == 'mask_weared_incorrect')}")
print(f"Total 'with_mask' labels: {sum(df_data.label == 'with_mask')}")
Total 'without_mask' labels: 717 Total 'mask_weared_incorrect' labels: 123 Total 'with_mask' labels: 3232
df_data.groupby('label')['label'].count().plot.bar(
title='class distribution'
)
<Axes: title={'center': 'class distribution'}, xlabel='label'>
def show_random_images_with_bbox(df):
all_images = os.listdir(image_directory)
random_image_filename = random.sample(all_images, 4)
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(10, 10))
for i, filename in enumerate(random_image_filename):
selected_df = df[df['filename'] == filename]
image = Image.open(image_directory + '/' + filename)
ax.flat[i].imshow(image)
ax.flat[i].axis(False)
image_bboxes = []
for df_index in range(0, len(selected_df)):
color = "g"
if selected_df.iloc[df_index].class_id == 1: color = "y"
elif selected_df.iloc[df_index].class_id == 2: color = "r"
x_min, y_min, x_max, y_max = selected_df.iloc[df_index].bboxes
rect = patches.Rectangle([x_min, y_min], x_max-x_min, y_max-y_min,
linewidth=2, edgecolor=color, facecolor="none")
ax.flat[i].add_patch(rect)
show_random_images_with_bbox(df_data)
# we need to convert our bbox format to yolo as the current one that we have is on pascal_voc
def pascal_voc_to_yolo_bbox(bbox_array, w, h):
x_min, y_min, x_max, y_max = bbox_array
x_center = ((x_max + x_min) / 2) / w
y_center = ((y_max + y_min) / 2) / h
width = (x_max - x_min) / w
height = (y_max - y_min) / h
return [x_center, y_center, width, height]
train_path = "/workspace/kaggle/working/datasets/train"
valid_path = "/workspace/kaggle/working/datasets/valid"
test_path = "/workspace/kaggle/working/datasets/test"
# os.mkdir("/workspace/kaggle/working")
# os.mkdir("/workspace/kaggle/working/datasets")
# os.mkdir(train_path)
# os.mkdir(valid_path)
# os.mkdir(test_path)
train, test = train_test_split(df_data.filename.unique(), test_size=0.2, random_state=23)
train, valid = train_test_split(train, test_size=0.15, random_state=23)
def copy_image_file(image_items, folder_name):
for image in image_items:
image_path = image_directory + "/" + image
new_image_path = os.path.join(folder_name, image)
shutil.copy(image_path, new_image_path)
def create_label_file(image_items, folder_name):
for image in image_items:
fileName = Path(image).stem
df = df_data[df_data['filename'] == image]
with open(folder_name + "/" + fileName +'.txt', 'w') as f:
for i in range(0, len(df)):
bbox = pascal_voc_to_yolo_bbox(df.iloc[i]['bboxes'], df.iloc[i]['width'], df.iloc[i]['height'])
bbox_text = " ".join(map(str, bbox))
txt = str(df.iloc[i]['class_id'])+ " " + bbox_text
f.write(txt)
if i != len(df) - 1:
f.write("\n")
copy_image_file(train, train_path)
copy_image_file(valid, valid_path)
copy_image_file(test, test_path)
create_label_file(train, train_path)
create_label_file(valid, valid_path)
create_label_file(test, test_path)
def walk_through_dir(filepath):
for dirpath, dirnames, filenames in os.walk(filepath):
print(f"There are {len(dirnames)} directories and {len(glob.glob(filepath + '/*.png', recursive = True))} images in '{dirpath}'.")
walk_through_dir(train_path)
walk_through_dir(valid_path)
walk_through_dir(test_path)
There are 0 directories and 579 images in '/workspace/kaggle/working/datasets/train'. There are 0 directories and 103 images in '/workspace/kaggle/working/datasets/valid'. There are 0 directories and 171 images in '/workspace/kaggle/working/datasets/test'.
classes = list(df_data.label.unique())
class_count = len(classes)
facemask_yaml = f"""
train: /workspace/kaggle/working/datasets/train
val: /workspace/kaggle/working/datasets/valid
test: /workspace/kaggle/working/datasets/test
nc: {class_count}
names:
0 : with_mask
1 : mask_weared_incorrect
2 : without_mask
"""
with open('/workspace/facemask.yaml', 'w') as f:
f.write(facemask_yaml)
%cat facemask.yaml
cat: facemask.yaml: No such file or directory
model = YOLO("/workspace/yolov8n.pt")
model.train(data="/workspace/facemask.yaml", epochs=50)
Ultralytics YOLOv8.0.218 🚀 Python-3.10.13 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB) engine/trainer: task=detect, mode=train, model=/workspace/yolov8n.pt, data=/workspace/facemask.yaml, epochs=50, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=/usr/src/ultralytics/runs/detect/train Overriding model.yaml nc=80 with nc=3 from n params module arguments 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] 1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2] 2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True] 3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2] 4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True] 5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2] 6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True] 7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2] 8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True] 9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5] 10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1] 12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1] 13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] 14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1] 15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1] 16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2] 17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1] 18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1] 19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2] 20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1] 21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1] 22 [15, 18, 21] 1 751897 ultralytics.nn.modules.head.Detect [3, [64, 128, 256]] Model summary: 225 layers, 3011433 parameters, 3011417 gradients, 8.2 GFLOPs Transferred 319/355 items from pretrained weights TensorBoard: Start with 'tensorboard --logdir /usr/src/ultralytics/runs/detect/train', view at http://localhost:6006/ Freezing layer 'model.22.dfl.conv.weight' AMP: running Automatic Mixed Precision (AMP) checks with YOLOv8n... Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...
100%|██████████| 6.23M/6.23M [00:00<00:00, 61.8MB/s]
AMP: checks passed ✅
train: Scanning /workspace/kaggle/working/datasets/train.cache... 579 images, 0 backgrounds, 0 corrupt: 100%|██████████| 579/579 [00:00<?, ?it/s]
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
val: Scanning /workspace/kaggle/working/datasets/valid.cache... 103 images, 0 backgrounds, 0 corrupt: 100%|██████████| 103/103 [00:00<?, ?it/s]
Plotting labels to /usr/src/ultralytics/runs/detect/train/labels.jpg...
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... optimizer: AdamW(lr=0.001429, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0) Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to /usr/src/ultralytics/runs/detect/train Starting training for 50 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/50 2.65G 1.76 2.795 1.433 50 640: 100%|██████████| 37/37 [00:03<00:00, 11.49it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 50%|█████ | 2/4 [00:00<00:00, 2.65it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:01<00:00, 3.53it/s]
all 103 497 0.0128 0.479 0.271 0.163
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/50 2.25G 1.325 1.45 1.095 8 640: 100%|██████████| 37/37 [00:02<00:00, 16.36it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 75%|███████▌ | 3/4 [00:00<00:00, 9.19it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.21it/s]
all 103 497 0.366 0.254 0.263 0.148
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/50 2.52G 1.328 1.304 1.078 26 640: 100%|██████████| 37/37 [00:02<00:00, 16.66it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 75%|███████▌ | 3/4 [00:00<00:00, 8.84it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 10.86it/s]
all 103 497 0.378 0.417 0.4 0.24
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/50 2.86G 1.254 1.166 1.051 22 640: 100%|██████████| 37/37 [00:02<00:00, 16.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 75%|███████▌ | 3/4 [00:00<00:00, 9.28it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.19it/s]
all 103 497 0.882 0.453 0.497 0.296
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/50 2.43G 1.223 1.115 1.062 5 640: 100%|██████████| 37/37 [00:02<00:00, 16.79it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 75%|███████▌ | 3/4 [00:00<00:00, 9.57it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.56it/s]
all 103 497 0.541 0.459 0.51 0.297
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/50 2.59G 1.232 1.03 1.041 12 640: 100%|██████████| 37/37 [00:02<00:00, 17.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.92it/s]
all 103 497 0.63 0.504 0.541 0.34
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/50 2.25G 1.179 0.9665 1.029 37 640: 100%|██████████| 37/37 [00:02<00:00, 17.14it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.08it/s]
all 103 497 0.878 0.466 0.55 0.321
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/50 2.39G 1.156 0.9201 1.029 17 640: 100%|██████████| 37/37 [00:02<00:00, 17.25it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.21it/s]
all 103 497 0.656 0.546 0.576 0.338
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/50 2.43G 1.141 0.8656 1.021 25 640: 100%|██████████| 37/37 [00:02<00:00, 17.11it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.23it/s]
all 103 497 0.489 0.556 0.532 0.333
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/50 2.72G 1.111 0.8482 1.011 15 640: 100%|██████████| 37/37 [00:02<00:00, 17.13it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.51it/s]
all 103 497 0.578 0.523 0.508 0.31
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/50 2.63G 1.116 0.8435 1.017 61 640: 100%|██████████| 37/37 [00:02<00:00, 17.10it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.51it/s]
all 103 497 0.74 0.525 0.613 0.354
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/50 2.86G 1.158 0.8064 1.005 12 640: 100%|██████████| 37/37 [00:02<00:00, 17.07it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.44it/s]
all 103 497 0.588 0.576 0.578 0.358
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/50 2.82G 1.118 0.7879 1.005 23 640: 100%|██████████| 37/37 [00:02<00:00, 17.03it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.57it/s]
all 103 497 0.665 0.624 0.595 0.375
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/50 2.32G 1.085 0.7421 0.9953 14 640: 100%|██████████| 37/37 [00:02<00:00, 17.20it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.57it/s]
all 103 497 0.663 0.625 0.646 0.394
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/50 2.72G 1.092 0.7381 0.9952 14 640: 100%|██████████| 37/37 [00:02<00:00, 16.78it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.53it/s]
all 103 497 0.735 0.525 0.571 0.353
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/50 2.5G 1.087 0.7119 0.9926 25 640: 100%|██████████| 37/37 [00:02<00:00, 17.09it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.59it/s]
all 103 497 0.732 0.571 0.581 0.368
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/50 2.53G 1.074 0.6945 0.9846 34 640: 100%|██████████| 37/37 [00:02<00:00, 16.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.67it/s]
all 103 497 0.735 0.6 0.612 0.376
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/50 2.39G 1.071 0.6978 0.9856 21 640: 100%|██████████| 37/37 [00:02<00:00, 17.15it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.65it/s]
all 103 497 0.777 0.593 0.63 0.382
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/50 2.89G 1.089 0.7136 0.994 42 640: 100%|██████████| 37/37 [00:02<00:00, 16.89it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.67it/s]
all 103 497 0.682 0.591 0.62 0.383
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/50 2.67G 1.076 0.6718 0.9826 10 640: 100%|██████████| 37/37 [00:02<00:00, 16.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.53it/s]
all 103 497 0.704 0.666 0.651 0.399
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/50 2.6G 1.054 0.6664 0.9746 28 640: 100%|██████████| 37/37 [00:02<00:00, 16.90it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.59it/s]
all 103 497 0.731 0.605 0.668 0.401
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/50 2.5G 1.061 0.6701 0.9791 22 640: 100%|██████████| 37/37 [00:02<00:00, 16.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.35it/s]
all 103 497 0.712 0.577 0.623 0.37
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/50 2.44G 1.038 0.6518 0.9716 31 640: 100%|██████████| 37/37 [00:02<00:00, 17.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.73it/s]
all 103 497 0.884 0.552 0.651 0.403
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/50 2.72G 1.037 0.6414 0.9709 58 640: 100%|██████████| 37/37 [00:02<00:00, 16.99it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.52it/s]
all 103 497 0.685 0.598 0.63 0.394
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/50 2.48G 1.035 0.6222 0.9671 22 640: 100%|██████████| 37/37 [00:02<00:00, 16.75it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.60it/s]
all 103 497 0.851 0.597 0.672 0.41
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/50 2.47G 0.9959 0.6055 0.9597 15 640: 100%|██████████| 37/37 [00:02<00:00, 16.87it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.62it/s]
all 103 497 0.886 0.58 0.67 0.395
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/50 2.75G 1.007 0.6049 0.9705 37 640: 100%|██████████| 37/37 [00:02<00:00, 16.96it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.75it/s]
all 103 497 0.843 0.586 0.684 0.424
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/50 2.5G 1.012 0.5959 0.9761 7 640: 100%|██████████| 37/37 [00:02<00:00, 17.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.47it/s]
all 103 497 0.75 0.636 0.662 0.409
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/50 2.92G 1.013 0.609 0.9586 42 640: 100%|██████████| 37/37 [00:02<00:00, 16.87it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.61it/s]
all 103 497 0.841 0.561 0.683 0.424
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/50 2.75G 0.9996 0.5865 0.9636 11 640: 100%|██████████| 37/37 [00:02<00:00, 17.00it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.53it/s]
all 103 497 0.776 0.642 0.679 0.422
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/50 2.41G 0.9865 0.5821 0.9626 10 640: 100%|██████████| 37/37 [00:02<00:00, 16.83it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.56it/s]
all 103 497 0.823 0.576 0.678 0.415
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/50 2.6G 0.989 0.5722 0.9474 17 640: 100%|██████████| 37/37 [00:02<00:00, 17.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.58it/s]
all 103 497 0.748 0.625 0.678 0.418
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/50 2.41G 0.9721 0.5687 0.9522 20 640: 100%|██████████| 37/37 [00:02<00:00, 16.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.36it/s]
all 103 497 0.787 0.602 0.668 0.42
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/50 2.73G 0.9729 0.5629 0.9509 20 640: 100%|██████████| 37/37 [00:02<00:00, 16.97it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.71it/s]
all 103 497 0.905 0.601 0.696 0.428
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/50 2.84G 0.9737 0.5528 0.9422 29 640: 100%|██████████| 37/37 [00:02<00:00, 16.86it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.70it/s]
all 103 497 0.884 0.601 0.684 0.423
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/50 3.1G 0.9629 0.5612 0.9418 14 640: 100%|██████████| 37/37 [00:02<00:00, 16.92it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.65it/s]
all 103 497 0.843 0.59 0.684 0.423
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/50 2.49G 0.9515 0.5527 0.9367 13 640: 100%|██████████| 37/37 [00:02<00:00, 16.77it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.37it/s]
all 103 497 0.751 0.616 0.663 0.411
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/50 2.41G 0.9437 0.5469 0.9453 17 640: 100%|██████████| 37/37 [00:02<00:00, 16.72it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.42it/s]
all 103 497 0.8 0.608 0.676 0.419
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/50 2.97G 0.9561 0.5294 0.9395 16 640: 100%|██████████| 37/37 [00:02<00:00, 16.82it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.51it/s]
all 103 497 0.792 0.674 0.711 0.431
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/50 2.42G 0.9412 0.5308 0.9379 46 640: 100%|██████████| 37/37 [00:02<00:00, 16.98it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.06it/s]
all 103 497 0.831 0.607 0.712 0.435
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/50 2.49G 0.9317 0.5035 0.9256 20 640: 100%|██████████| 37/37 [00:02<00:00, 14.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.81it/s]
all 103 497 0.802 0.609 0.717 0.438
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/50 2.62G 0.9187 0.5101 0.9223 60 640: 100%|██████████| 37/37 [00:02<00:00, 16.93it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 75%|███████▌ | 3/4 [00:00<00:00, 9.71it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.26it/s]
all 103 497 0.797 0.607 0.683 0.419
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/50 2.65G 0.9158 0.4996 0.9236 12 640: 100%|██████████| 37/37 [00:02<00:00, 16.91it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.80it/s]
all 103 497 0.746 0.622 0.678 0.421
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/50 2.37G 0.9093 0.4895 0.9125 14 640: 100%|██████████| 37/37 [00:02<00:00, 17.31it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.40it/s]
all 103 497 0.765 0.621 0.68 0.411
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/50 2.45G 0.8972 0.4761 0.9221 13 640: 100%|██████████| 37/37 [00:02<00:00, 17.04it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.42it/s]
all 103 497 0.737 0.613 0.707 0.436
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/50 2.66G 0.8796 0.4654 0.9131 15 640: 100%|██████████| 37/37 [00:02<00:00, 17.08it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.62it/s]
all 103 497 0.776 0.634 0.713 0.442
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/50 2.51G 0.8806 0.4632 0.9135 10 640: 100%|██████████| 37/37 [00:02<00:00, 17.02it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.33it/s]
all 103 497 0.816 0.608 0.718 0.441
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/50 2.45G 0.8809 0.4612 0.9126 7 640: 100%|██████████| 37/37 [00:02<00:00, 17.05it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.78it/s]
all 103 497 0.79 0.615 0.712 0.437
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/50 2.47G 0.8654 0.4533 0.914 7 640: 100%|██████████| 37/37 [00:02<00:00, 17.07it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 12.12it/s]
all 103 497 0.827 0.639 0.715 0.441
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/50 2.5G 0.8618 0.4516 0.9133 3 640: 100%|██████████| 37/37 [00:02<00:00, 17.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 11.79it/s]
all 103 497 0.856 0.639 0.728 0.449
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
50 epochs completed in 0.040 hours. Optimizer stripped from /usr/src/ultralytics/runs/detect/train/weights/last.pt, 6.2MB Optimizer stripped from /usr/src/ultralytics/runs/detect/train/weights/best.pt, 6.2MB Validating /usr/src/ultralytics/runs/detect/train/weights/best.pt... Ultralytics YOLOv8.0.218 🚀 Python-3.10.13 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB) Model summary (fused): 168 layers, 3006233 parameters, 0 gradients, 8.1 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 25%|██▌ | 1/4 [00:00<00:00, 8.48it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 4/4 [00:00<00:00, 5.95it/s]
all 103 497 0.857 0.639 0.723 0.448
with_mask 103 409 0.971 0.891 0.953 0.677
mask_weared_incorrect 103 12 0.747 0.417 0.488 0.256
without_mask 103 76 0.853 0.609 0.728 0.412
Speed: 0.4ms preprocess, 0.8ms inference, 0.0ms loss, 0.5ms postprocess per image
Results saved to /usr/src/ultralytics/runs/detect/train
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1, 2])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f79a60e5de0>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
curves_results: [[array([ 0, 0.001001, 0.002002, 0.003003, 0.004004, 0.005005, 0.006006, 0.007007, 0.008008, 0.009009, 0.01001, 0.011011, 0.012012, 0.013013, 0.014014, 0.015015, 0.016016, 0.017017, 0.018018, 0.019019, 0.02002, 0.021021, 0.022022, 0.023023,
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[ 0.91667, 0.91667, 0.91667, ..., 0, 0, 0],
[ 0.88158, 0.88158, 0.86842, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: 0.4759270853385126
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.6774, 0.2564, 0.41166])
names: {0: 'with_mask', 1: 'mask_weared_incorrect', 2: 'without_mask'}
plot: True
results_dict: {'metrics/precision(B)': 0.8568229287332693, 'metrics/recall(B)': 0.6387382788675344, 'metrics/mAP50(B)': 0.7229090717535667, 'metrics/mAP50-95(B)': 0.44848464240350655, 'fitness': 0.4759270853385126}
save_dir: PosixPath('/usr/src/ultralytics/runs/detect/train')
speed: {'preprocess': 0.4245193259229938, 'inference': 0.7751520397593674, 'loss': 0.000365729470854824, 'postprocess': 0.4634625703385733}
task: 'detect'
model.val(data="/workspace/facemask.yaml")
Ultralytics YOLOv8.0.218 🚀 Python-3.10.13 torch-2.1.0 CUDA:0 (NVIDIA GeForce RTX 3090, 24260MiB)
val: Scanning /workspace/kaggle/working/datasets/valid.cache... 103 images, 0 backgrounds, 0 corrupt: 100%|██████████| 103/103 [00:00<?, ?it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 0%| | 0/7 [00:00<?, ?it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 14%|█▍ | 1/7 [00:00<00:01, 3.01it/s]libpng warning: iCCP: Not recognizing known sRGB profile that has been edited
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 7/7 [00:01<00:00, 5.35it/s]
all 103 497 0.849 0.642 0.714 0.443
with_mask 103 409 0.969 0.888 0.953 0.678
mask_weared_incorrect 103 12 0.739 0.417 0.466 0.252
without_mask 103 76 0.84 0.623 0.724 0.399
Speed: 0.1ms preprocess, 7.0ms inference, 0.0ms loss, 0.4ms postprocess per image
Results saved to /usr/src/ultralytics/runs/detect/train3
ultralytics.utils.metrics.DetMetrics object with attributes:
ap_class_index: array([0, 1, 2])
box: ultralytics.utils.metrics.Metric object
confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f79a20e2c80>
curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']
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0.84084, 0.84184, 0.84284, 0.84384, 0.84484, 0.84585, 0.84685, 0.84785, 0.84885, 0.84985, 0.85085, 0.85185, 0.85285, 0.85385, 0.85485, 0.85586, 0.85686, 0.85786, 0.85886, 0.85986, 0.86086, 0.86186, 0.86286, 0.86386,
0.86486, 0.86587, 0.86687, 0.86787, 0.86887, 0.86987, 0.87087, 0.87187, 0.87287, 0.87387, 0.87487, 0.87588, 0.87688, 0.87788, 0.87888, 0.87988, 0.88088, 0.88188, 0.88288, 0.88388, 0.88488, 0.88589, 0.88689, 0.88789,
0.88889, 0.88989, 0.89089, 0.89189, 0.89289, 0.89389, 0.89489, 0.8959, 0.8969, 0.8979, 0.8989, 0.8999, 0.9009, 0.9019, 0.9029, 0.9039, 0.9049, 0.90591, 0.90691, 0.90791, 0.90891, 0.90991, 0.91091, 0.91191,
0.91291, 0.91391, 0.91491, 0.91592, 0.91692, 0.91792, 0.91892, 0.91992, 0.92092, 0.92192, 0.92292, 0.92392, 0.92492, 0.92593, 0.92693, 0.92793, 0.92893, 0.92993, 0.93093, 0.93193, 0.93293, 0.93393, 0.93493, 0.93594,
0.93694, 0.93794, 0.93894, 0.93994, 0.94094, 0.94194, 0.94294, 0.94394, 0.94494, 0.94595, 0.94695, 0.94795, 0.94895, 0.94995, 0.95095, 0.95195, 0.95295, 0.95395, 0.95495, 0.95596, 0.95696, 0.95796, 0.95896, 0.95996,
0.96096, 0.96196, 0.96296, 0.96396, 0.96496, 0.96597, 0.96697, 0.96797, 0.96897, 0.96997, 0.97097, 0.97197, 0.97297, 0.97397, 0.97497, 0.97598, 0.97698, 0.97798, 0.97898, 0.97998, 0.98098, 0.98198, 0.98298, 0.98398,
0.98498, 0.98599, 0.98699, 0.98799, 0.98899, 0.98999, 0.99099, 0.99199, 0.99299, 0.99399, 0.99499, 0.996, 0.997, 0.998, 0.999, 1]), array([[ 0.97555, 0.97555, 0.97066, ..., 0, 0, 0],
[ 0.91667, 0.91667, 0.91667, ..., 0, 0, 0],
[ 0.88158, 0.88158, 0.86842, ..., 0, 0, 0]]), 'Confidence', 'Recall']]
fitness: 0.47024924995957046
keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']
maps: array([ 0.67824, 0.25246, 0.39872])
names: {0: 'with_mask', 1: 'mask_weared_incorrect', 2: 'without_mask'}
plot: True
results_dict: {'metrics/precision(B)': 0.8493422667212651, 'metrics/recall(B)': 0.6423233907030225, 'metrics/mAP50(B)': 0.714242302254997, 'metrics/mAP50-95(B)': 0.44313891081563417, 'fitness': 0.47024924995957046}
save_dir: PosixPath('/usr/src/ultralytics/runs/detect/train3')
speed: {'preprocess': 0.13610460225818227, 'inference': 7.0161194477266475, 'loss': 0.0005694268976600425, 'postprocess': 0.42321149585316487}
task: 'detect'
confusion_matrix = Image.open("/usr/src/ultralytics/runs/detect/train/confusion_matrix_normalized.png")
plt.figure(figsize=(20,10))
plt.imshow(confusion_matrix)
plt.axis(False)
plt.show()
val_label = Image.open("/usr/src/ultralytics/runs/detect/train/val_batch0_labels.jpg")
val_pred = Image.open("/usr/src/ultralytics/runs/detect/train/val_batch0_pred.jpg")
plt.figure(figsize=(20,10))
plt.imshow(val_label)
plt.title("Label")
plt.axis(False)
plt.show()
plt.figure(figsize=(20,10))
plt.imshow(val_pred)
plt.title("Prediction")
plt.axis(False)
plt.show()
# f1_curve = Image.open("/usr/src/ultralytics/runs/detect/train/F1_curve.png")
pr_curve = Image.open("/usr/src/ultralytics/runs/detect/train/PR_curve.png")
# plt.figure(figsize=(20,10))
# plt.imshow(f1_curve)
# plt.title("F1_Curve")
# plt.axis(False)
# plt.show()
plt.figure(figsize=(20,10))
plt.imshow(pr_curve)
plt.title("PR_Curve")
plt.axis(False)
plt.show()
model = YOLO(model="/usr/src/ultralytics/runs/detect/train/weights/best.pt")
filenames = glob.glob(test_path+"/*.png", recursive=False)
test_image1 = cv2.imread(filenames[3])
test_image2 = cv2.imread(filenames[4])
results = model.predict([test_image1, test_image2], save=True, line_thickness=1)
WARNING ⚠️ 'line_thickness' is deprecated and will be removed in 'ultralytics 8.2' in the future. Please use 'line_width' instead.
0: 448x640 17 with_masks, 5 without_masks, 1: 448x640 3 with_masks, 4.7ms
Speed: 0.8ms preprocess, 2.4ms inference, 0.7ms postprocess per image at shape (1, 3, 448, 640)
Results saved to /usr/src/ultralytics/runs/detect/predict3
predicted_image = Image.open("/usr/src/ultralytics/runs/detect/predict/image0.jpg")
plt.figure(figsize=(10,10))
plt.imshow(predicted_image)
plt.title("Prediction")
plt.axis(False)
plt.show()
predicted_image = Image.open("/usr/src/ultralytics/runs/detect/predict/image1.jpg")
plt.figure(figsize=(10,10))
plt.imshow(predicted_image)
plt.title("Prediction")
plt.axis(False)
plt.show()
